Our main objective is to provide a system, which is transparent to the user and scalable at the same time.

We chose to build a hybrid system consisting of collaborative-based and content-based recommenders. This gives us the chance to exploit the content base as well as give fresh recommendations. Recommendations are a combination of the pages obtained from the user's profile, commonly referred to as their interests, and also pages from the user's browsing history. 

We use closed networks for security purposes but use one-way communication for friend requests thereby avoiding giving bad recommendations.

Our system uses semantic information to recommend Wikipedia pages by using the DBpedia framework, as previously mentioned. This is very different to other recommenders such as Amazon or Facebook. Having access to the semantically-tagged articles by using DBpedia means that we can decisively determine what the contents of the page are and hence the relevance of recommendations is higher and more accurate.

Chapter~\ref{ch3}, Development, will explain the above aspects in depth.
